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• COMPARATIVE ANALYSIS OF MISSING DATA RECOVERY METHODS

  A.A. Sorokin , A. V. Dagaev , I. M. Borodyansky

  2020-11-22

  Abstract ▼

  In recent decades, the methods of system analysis have been developing qualitatively. It is
  associated with an increase in the rate of technical development, the densification of time processes,
  the rapid growth of accumulated information and new capabilities of computer technology.
  These include methods for analyzing large amounts of data, methods of data mining, methods of
  analytical modeling, methods of parallel data processing, neural network methods, forecasting
  methods, and others. The presented methods make it possible to quickly and efficiently process
  heterogeneous clusters of information, accumulate and synthesize data, generalize and classify
  information. The last of the presented methods are methods of interpolation and extrapolation of
  lost, damaged or missing information. These methods allow to structure, restore and model information
  based on statistical data, mathematical and algorithmic methods. Thus, the article deals
  with the problem of recovering missing data in graphic and complex objects. Literary sources on
  the problems under consideration are given. They provide extensive information on the topic under
  consideration: present genetic algorithms used for spatial interpolation; the solution of problems
  of heterogeneity of interpolation of seismic data is considered; it is described the use of
  spline approximation to calculate the characteristics of nonlinear electronic components; the
  method of constructing a model of three-dimensional parametric rational bodies using generalized
  Bezier interpolation is analyzed, which allows modeling the shape of a body and anisotropic
  space; methods using fuzzy linear equations are described, which are widespread in computer
  vision; the method of adaptive interpolation based on the gradient and taking into account the
  local gradient of the original image is investigated. It is made comparing several common methods
  of interpolation and data restoration, in article, such as: bilinear interpolation, Bezier surface.
  Each method and features of its application within the framework of the experiment are briefly
  described. The result of a series of experiments with the presented methods with different numbers
  of tests is presented. In conclusion, summary is drawn about the rationality of choosing one of the
  proposed methods without the use of a long field experiment in each case.

• OPTIMIZATION OF THE COMPUTATIONAL SCHEME FOR THE INTERPOLATION OF DECADAL METEOROLOGICAL DATA BY INVERSE DISTANCE WEIGHTING WITH PARALLEL PROCESSING OF MULTIPLE TIME SLICES

  О.М. Golozubov , А.V. Kozlovskiy , E.V. Melnik , Y.E. Melnik , А.N. Samoylov

  22-32

  2025-12-30

  Abstract ▼

  The present study is devoted to solving the problem of computational inefficiency in spatial interpolation of large arrays of decadal meteorological data using the inverse distance weighting method. Traditional approaches involving sequential and independent processing of each time slice demonstrate a linear increase in execution time and significant RAM consumption, which becomes a critical barrier to the rapid construction of detailed and geographically linked raster fields in GeoTIFF format. This significantly limits the use of the method in tasks requiring rapid processing of long-term data archives. The purpose of this work is to develop and validate an optimized computational scheme that can radically reduce time costs while maintaining the completeness and accuracy of the results. The key scientific novelty of the proposed approach lies in the fundamental rethinking of the computational process. Instead of repeating identical operations many times, a scheme is proposed based on a single calculation of the full vector of geodetic distances from each grid cell to all weather stations. This most resource-intensive operation is performed only once. Subsequently, the resulting distance vector is applied to all time slices (decades) to calculate the interpolated values, which eliminates the main computational redundancy and ensures a sublinear dependence of processing time on the number of decades. To further improve performance, a parallel processing mechanism is used at the CPU level, implemented by dynamically dividing the raster into independent computing units (batches). The size of the batches is adaptively adjusted taking into account the available RAM, which guarantees the stability and scalability of the solution on systems of various capacities. The testing of the method on real meteorological data for the period 2015-2024 demonstrated a radical reduction in the execution time. In particular, processing ten decade time slices on a standard laptop takes less than 3.5 minutes, and on a server platform it takes about 3 minutes, which represents a multiple acceleration compared to traditional implementations. Thus, the developed solution makes the operational processing of large spatial and temporal meteorological arrays a reality for a wide range of researchers, opening up new opportunities for climate monitoring, agrometeorology and geoinformation analysis without the need for specialized expensive equipment